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Popular New Releases in Pytorch
models | TensorFlow Official Models 2.7.1 |
transformers | v4.18.0: Checkpoint sharding, vision models |
pytorch | PyTorch 1.11, TorchData, and functorch are now available |
d2l-zh | 2.0.0-beta0版发布 |
yolov5 | v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference |
models TensorFlow Official Models 2.7.1 |
transformers v4.18.0: Checkpoint sharding, vision models |
pytorch PyTorch 1.11, TorchData, and functorch are now available |
d2l-zh 2.0.0-beta0版发布 |
yolov5 v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference |
Popular Libraries in Pytorch
by tensorflow python
73392 NOASSERTION
Models and examples built with TensorFlow
by huggingface python
61400 Apache-2.0
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
by pytorch c++
55457 NOASSERTION
Tensors and Dynamic neural networks in Python with strong GPU acceleration
by DefinitelyTyped typescript
38679 NOASSERTION
The repository for high quality TypeScript type definitions.
by fighting41love python
33333
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代表性的数据集-基准(预训练)模型-语料库-baseline-工具包-排行榜、PySS3：面向可解释AI的SS3文本分类器机器可视化工具 、中文NLP数据集列表、COPE - 格律诗编辑程序、doccano：基于网页的开源协同多语言文本标注工具 、PreNLP：自然语言预处理库、简单的简历解析器，用来从简历中提取关键信息、用于中文闲聊的GPT2模型：GPT2-chitchat、基于检索聊天机器人多轮响应选择相关资源列表(Leaderboards、Datasets、Papers)、(Colab)抽象文本摘要实现集锦(教程 、词语拼音数据、高效模糊搜索工具、NLP数据增广资源集、微软对话机器人框架 、 GitHub Typo Corpus：大规模GitHub多语言拼写错误/语法错误数据集、TextCluster：短文本聚类预处理模块 Short text cluster、面向语音识别的中文文本规范化、BLINK：最先进的实体链接库、BertPunc：基于BERT的最先进标点修复模型、Tokenizer：快速、可定制的文本词条化库、中文语言理解测评基准，包括代表性的数据集、基准(预训练)模型、语料库、排行榜、spaCy 医学文本挖掘与信息提取 、 NLP任务示例项目代码集、 python拼写检查库、chatbot-list - 行业内关于智能客服、聊天机器人的应用和架构、算法分享和介绍、语音质量评价指标(MOSNet, BSSEval, STOI, PESQ, SRMR)、 用138GB语料训练的法文RoBERTa预训练语言模型 、BERT-NER-Pytorch：三种不同模式的BERT中文NER实验、无道词典 - 有道词典的命令行版本，支持英汉互查和在线查询、2019年NLP亮点回顾、 Chinese medical dialogue data 中文医疗对话数据集 、最好的汉字数字(中文数字)-阿拉伯数字转换工具、 基于百科知识库的中文词语多词义/义项获取与特定句子词语语义消歧、awesome-nlp-sentiment-analysis - 情感分析、情绪原因识别、评价对象和评价词抽取、LineFlow：面向所有深度学习框架的NLP数据高效加载器、中文医学NLP公开资源整理 、MedQuAD：(英文)医学问答数据集、将自然语言数字串解析转换为整数和浮点数、Transfer Learning in Natural Language Processing (NLP) 、面向语音识别的中文/英文发音辞典、Tokenizers：注重性能与多功能性的最先进分词器、CLUENER 细粒度命名实体识别 Fine Grained Named Entity Recognition、 基于BERT的中文命名实体识别、中文谣言数据库、NLP数据集/基准任务大列表、nlp相关的一些论文及代码, 包括主题模型、词向量(Word Embedding)、命名实体识别(NER)、文本分类(Text Classificatin)、文本生成(Text Generation)、文本相似性(Text Similarity)计算等，涉及到各种与nlp相关的算法，基于keras和tensorflow 、Python文本挖掘/NLP实战示例、 Blackstone：面向非结构化法律文本的spaCy pipeline和NLP模型通过同义词替换实现文本“变脸” 、中文 预训练 ELECTREA 模型: 基于对抗学习 pretrain Chinese Model 、albert-chinese-ner - 用预训练语言模型ALBERT做中文NER 、基于GPT2的特定主题文本生成/文本增广、开源预训练语言模型合集、多语言句向量包、编码、标记和实现：一种可控高效的文本生成方法、 英文脏话大列表 、attnvis：GPT2、BERT等transformer语言模型注意力交互可视化、CoVoST：Facebook发布的多语种语音-文本翻译语料库，包括11种语言(法语、德语、荷兰语、俄语、西班牙语、意大利语、土耳其语、波斯语、瑞典语、蒙古语和中文)的语音、文字转录及英文译文、Jiagu自然语言处理工具 - 以BiLSTM等模型为基础，提供知识图谱关系抽取 中文分词 词性标注 命名实体识别 情感分析 新词发现 关键词 文本摘要 文本聚类等功能、用unet实现对文档表格的自动检测，表格重建、NLP事件提取文献资源列表 、 金融领域自然语言处理研究资源大列表、CLUEDatasetSearch - 中英文NLP数据集：搜索所有中文NLP数据集，附常用英文NLP数据集 、medical_NER - 中文医学知识图谱命名实体识别 、(哈佛)讲因果推理的免费书、知识图谱相关学习资料/数据集/工具资源大列表、Forte：灵活强大的自然语言处理pipeline工具集 、Python字符串相似性算法库、PyLaia：面向手写文档分析的深度学习工具包、TextFooler：针对文本分类/推理的对抗文本生成模块、Haystack：灵活、强大的可扩展问答(QA)框架、中文关键短语抽取工具
by CorentinJ python
32619 NOASSERTION
Clone a voice in 5 seconds to generate arbitrary speech in real-time
by apachecn python
31033 NOASSERTION
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
by d2l-ai python
30655 Apache-2.0
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被55个国家的300所大学用于教学。
by floodsung python
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Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
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QUESTION
Syntax for making objects callable in python
Asked 2022-Mar-26 at 18:08I understand that in python user-defined objects can be made callable by defining a __call__() method in the class definition. For example,
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14I was looking at how pytorch makes the forward() method of a nn.Module object be called implicitly when the object is called and saw some syntax I didn't understand.
In the line that supposedly defines the __call__ method the syntax used is,
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14__call__ : Callable[..., Any] = _call_impl
15This seemed like a combination of an annotation (keyword Callable[ following : ignored by python) and a value of _call_impl which we want to be called when __call__ is invoked, and my guess is that this is a shorthand for,
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14__call__ : Callable[..., Any] = _call_impl
15def __call__(self, *args, **kwargs):
16 return self._call_impl(*args, **kwargs)
17but wanted to understand clearly how this method of defining functions worked.
My question is: When would we want to use such a definition of callable attributes of a class instead of the usual def myfunc(self, *args, **kwargs)
ANSWER
Answered 2022-Mar-26 at 18:08Functions are normal first-class objects in python. The name to with which you define a function object, e.g. with a def statement, is not set in stone, any more than it would be for an int or list. Just as you can do
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14__call__ : Callable[..., Any] = _call_impl
15def __call__(self, *args, **kwargs):
16 return self._call_impl(*args, **kwargs)
17a = [1, 2, 3]
18b = a
19to access the elements of a through the name b, you can do the same with functions. In your first example, you could replace
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14__call__ : Callable[..., Any] = _call_impl
15def __call__(self, *args, **kwargs):
16 return self._call_impl(*args, **kwargs)
17a = [1, 2, 3]
18b = a
19def __call__(self, input1):
20 self.my_function(input1)
21with the much simpler
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14__call__ : Callable[..., Any] = _call_impl
15def __call__(self, *args, **kwargs):
16 return self._call_impl(*args, **kwargs)
17a = [1, 2, 3]
18b = a
19def __call__(self, input1):
20 self.my_function(input1)
21__call__ = my_function
22You would need to put this line after the definition of my_function.
The key differences between the two implementations is that def __call__(... creates a new function. __call__ = ... simply binds the name __call__ to the same object as my_function. The noticeable difference is that if you do __call__.__name__, the first version will show __call__, while the second will show my_function, since that's what gets assigned by a def statement.
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QUESTION
Syntax for making objects callable in python
Asked 2022-Mar-26 at 18:08I understand that in python user-defined objects can be made callable by defining a __call__() method in the class definition. For example,
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14I was looking at how pytorch makes the forward() method of a nn.Module object be called implicitly when the object is called and saw some syntax I didn't understand.
In the line that supposedly defines the __call__ method the syntax used is,
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14__call__ : Callable[..., Any] = _call_impl
15This seemed like a combination of an annotation (keyword Callable[ following : ignored by python) and a value of _call_impl which we want to be called when __call__ is invoked, and my guess is that this is a shorthand for,
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14__call__ : Callable[..., Any] = _call_impl
15def __call__(self, *args, **kwargs):
16 return self._call_impl(*args, **kwargs)
17but wanted to understand clearly how this method of defining functions worked.
My question is: When would we want to use such a definition of callable attributes of a class instead of the usual def myfunc(self, *args, **kwargs)
ANSWER
Answered 2022-Mar-26 at 18:08Functions are normal first-class objects in python. The name to with which you define a function object, e.g. with a def statement, is not set in stone, any more than it would be for an int or list. Just as you can do
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14__call__ : Callable[..., Any] = _call_impl
15def __call__(self, *args, **kwargs):
16 return self._call_impl(*args, **kwargs)
17a = [1, 2, 3]
18b = a
19to access the elements of a through the name b, you can do the same with functions. In your first example, you could replace
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14__call__ : Callable[..., Any] = _call_impl
15def __call__(self, *args, **kwargs):
16 return self._call_impl(*args, **kwargs)
17a = [1, 2, 3]
18b = a
19def __call__(self, input1):
20 self.my_function(input1)
21with the much simpler
1class MyClass:
2 def __init__(self):
3 pass
4
5 def __call__(self, input1):
6 self.my_function(input1)
7
8 def my_function(self, input1):
9 print(f"MyClass - print {input1}")
10
11my_obj = MyClass()
12# same as calling my_obj.my_function("haha")
13my_obj("haha") # prints "MyClass - print haha"
14__call__ : Callable[..., Any] = _call_impl
15def __call__(self, *args, **kwargs):
16 return self._call_impl(*args, **kwargs)
17a = [1, 2, 3]
18b = a
19def __call__(self, input1):
20 self.my_function(input1)
21__call__ = my_function
22You would need to put this line after the definition of my_function.
The key differences between the two implementations is that def __call__(... creates a new function. __call__ = ... simply binds the name __call__ to the same object as my_function. The noticeable difference is that if you do __call__.__name__, the first version will show __call__, while the second will show my_function, since that's what gets assigned by a def statement.
Community Discussions contain sources that include Stack Exchange Network
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